Provides a web-based tool for monitoring and managing resources, users and jobs. Serves as an interface for configuring and managing high-availability services, and defining alerts when thresholds are exceeded.

Scalability for running complex analytics faster

Lets you manage a wide variety of SAS and open source jobs across grid environments for optimal resource utilization and faster processing. Divide individual SAS jobs into subtasks that are executed in parallel to accelerate processing and increase workload throughput.

Meet peak computing demands reliably and cost-effectively.

Balance your workload.

SAS Grid Manager gives IT the flexibility to meet service level commitments by easily reassigning computing resources to meet peak workloads or changing business demands. A central point of control lets you easily administer policies, programs, queues and job prioritization across users and applications to achieve business goals under a given set of constraints.

Be ready for anything with a highly available SAS computing environment.

Having multiple servers in a grid computing environment enables jobs to run on the best available resource. If a server fails, its jobs can be transitioned seamlessly to another server. IT staff can also perform maintenance on specific servers, as well as introduce additional computing resources, without interrupting analytics jobs or disrupting the business. And because analysts in today's increasingly diverse analytics ecosystems are using a variety of programming languages, SAS Grid Manager lets you manage all your jobs, in SAS and other languages, ensuring all your analytics run quickly.

Divide and conquer.

Multiprocessing capabilities let you divide individual jobs into subtasks that run in parallel, reducing processing time. This is particularly effective for analytics programs with large data sets and long run times, as well as those with repetitive runs of independent tasks running against large data sets. You can take advantage of all available computing resources now and cost-effectively scale out as needed, adding capacity in single processing units with commodity hardware. There’s no need to size today’s environment for anticipated future needs.